KNN分类在breast cancer数据集上的应用在jupyter notebook中实现

好的，下面是在Jupyter Notebook中使用KNN算法对Breast Cancer数据集进行分类的代码：

首先导入需要的库：

import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

加载Breast Cancer数据集：

data = load_breast_cancer()

将数据集转换为DataFrame格式：

df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target

将数据集分为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(df[data.feature_names], df['target'], test_size=0.2, random_state=42)

初始化KNN分类器：

knn = KNeighborsClassifier(n_neighbors=5)

在训练集上训练KNN分类器：

knn.fit(X_train, y_train)

在测试集上进行预测：

y_pred = knn.predict(X_test)

计算模型的准确率：

accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

完整代码如下：

import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 加载Breast Cancer数据集
data = load_breast_cancer()

# 将数据集转换为DataFrame格式
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.target

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(df[data.feature_names], df['target'], test_size=0.2, random_state=42)

# 初始化KNN分类器
knn = KNeighborsClassifier(n_neighbors=5)

# 在训练集上训练KNN分类器
knn.fit(X_train, y_train)

# 在测试集上进行预测
y_pred = knn.predict(X_test)

# 计算模型的准确率
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

运行代码后，即可得到模型在测试集上的准确率。